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Accurate Bayesian Prediction of Cardiovascular-Related Mortality Using Ambulatory Blood Pressure Measurements

  • James O’Neill
  • Michael G. Madden
  • Eamon Dolan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

Abstract

Hypertension is the leading cause of cardiovascular-related mortality (CVRM), affecting approximately 1 billion people worldwide. To enable patients at significant risk of CVRM to be treated appropriately, it is essential to correctly diagnose hypertensive patients at an early stage. Our work achieves highly accurate risk scores and classification using 24-h Ambulatory Blood Pressure Monitoring (ABPM) to improve predictions. It involves two stages: (1) time series feature extraction using sliding window clustering techniques and transformations on raw ABPM signals, and (2) incorporation of these features and patient attributes into a probabilistic classifier to predict whether patients will die from cardiovascular-related illness within a median period of 8 years. When applied to a cohort of 5644 hypertensive patients, with 20% held out for testing, a K2 Bayesian network classifier (BNC) achieves 89.67% test accuracy on the final evaluation. We evaluate various BNC approaches with and without ABPM features, concluding that best performance arises from combining APBM features and clinical features in a BNC that represents multiple interactions, learned with some human knowledge in the form of arc constraints.

Keywords

Bayesian network Ambulatory Blood Pressure Monitoring Hypertension 

References

  1. 1.
    Finegold, J.A., Asaria, P., Francis, D.P.: Mortality from ischaemic heart disease by country, region, and age: statistics from World Health Organisation and United Nations. Int. J. Cardiol. 168(2), 934–945 (2013)CrossRefGoogle Scholar
  2. 2.
    Kabir, Z., Perry, I.J., Critchley, J., O’Flaherty, M., Capewell, S., Bennett, K.: Modelling coronary heart disease mortality declines in the Republic of Ireland, 1985–2006. Int. J. Cardiol. 168(3), 2462–2467 (2013)CrossRefGoogle Scholar
  3. 3.
    Mozaffarian, D., et al.: Executive summary: heart disease and stroke statistics-2016 update: a report from the AmerIcan Heart Association. Circulation 133(4), 447 (2016)CrossRefGoogle Scholar
  4. 4.
    Soni, J., Ansari, U., Sharma, D., Soni, S.: Intelligent and effective heart disease prediction system using weighted associative classifiers. Int. J. Comput. Sci. Eng. 3(6), 2385–2392 (2011)Google Scholar
  5. 5.
    Bhatla, N., Jyoti, K.: An analysis of heart disease prediction using different data mining techniques. Int. J. Eng. 1(8), 1–4 (2012)Google Scholar
  6. 6.
    Austin, P.C., Tu, J.V., Ho, J.E., Levy, D., Lee, D.S.: Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. J. Clin. Epidemiol. 66(4), 398–407 (2013)CrossRefGoogle Scholar
  7. 7.
    Nagarajan, R., Scutari, M., Lèbre, S.: Bayesian Networks in R, vol. 122. Springer, Heidelberg (2013). pp. 125–127CrossRefMATHGoogle Scholar
  8. 8.
    Madden, M.G.: On the classification performance of TAN and general Bayesian networks. Knowl.-Based Syst. 22(7), 489–495 (2009)CrossRefGoogle Scholar
  9. 9.
    Dolan, E., Stanton, A., Thijs, L., Hinedi, K., Atkins, N., McClory, S., Den Hond, E., McCormack, P., Staessen, J.A., O’Brien, E.: Superiority of ambulatory over clinic blood pressure measurement in predicting mortality. Hypertension 46(1), 156–161 (2005)CrossRefGoogle Scholar
  10. 10.
    Freitas, A.A.: Comprehensible classification models: a position paper. ACM SIGKDD Explor. Newslett. 15(1), 1–10 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • James O’Neill
    • 1
  • Michael G. Madden
    • 1
  • Eamon Dolan
    • 2
  1. 1.College Engineering and InformaticsNational University of IrelandGalwayIreland
  2. 2.Stroke and Hypertension UnitConnolly HospitalDublinIreland

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